Spectral data arising from spectroscopic analysis provides practitioners with a wealth of detailed information about the identity, structure, and concentration of samples or constituents of samples. Spectral data derives from the detected and recorded energy change of a molecule through the emission or absorption of a photon.
In particular, practitioners focus upon a molecule's vibration. Atoms within a molecular species vibrate back and forth about an average distance. Absorption of light by an atom at an appropriate energy causes the atoms to become excited, elevating the atom to a higher vibration level. The excitation of the atoms to an excited state occurs only at certain discrete energy levels, which are characteristic for that particular molecule. Infrared absorption spectroscopy is particularly useful for performing this type of analysis. In absorption spectroscopy, the net absorption of incident radiation at various wavelengths is measured.
Radiation passing through a sample is attenuated depending upon the pathlength traveled by the radiation and the strength of absorptions at various individual wavelengths for constituents within that particular sample. Recording and mapping the relative strength of the absorption versus wavelength results in a unique absorption “fingerprint” for that particular sample.
Cataloging infrared absorption permits practitioners to readily identify unknown samples by cross-referencing their samples of interest with a cataloged database. Matches between the spectrum of a compound of known structure and that of an unknown structure unambiguously identify the latter. This is an illustration of the qualitative aspects of spectrometry. Similarly, spectrometry also aids practitioners in quantitative analysis of known compounds. In illustration, calibration of a spectrophotometer on a known analyte of known concentration permits the accurate measurement of the same analyte of unknown concentration.
Most absorption spectroscopic instruments consist of five components: 1) a source of radiant energy, 2) a wavelength selector that permits the isolation of a restricted wavelength region (i.e., a monochromator), 3) one or more sample containers, 4) a radiation detector which converts radiant energy to a measurable signal (usually electrical), and 5) a signal processor and readout.
One application area for multivariate quantitative spectroscopy is the measurement of tissue attributes or analytes noninvasively. A specific application is the measurement of glucose noninvasively for subjects with diabetes. This application is difficult due to the complexity of the tissue, a turbid media, and the small size of the glucose signal. For the measurement of analytes with small concentrations in turbid media, care must be taken to minimize spectroscopic variances that overlap with the absorbance spectrum of the analyte of interest. Spectroscopic interferences are those spectroscopic variances unrelated to the analyte of interest but present during calibration development or during the measurement. Spectral interferences that overlap with or appear similar to the analyte of interest are especially bothersome. Spectroscopic interferences or variances can be classified into four general groups: 1) measurement variance; 2) physiological variance; 3) instrument variance; and 4) sampling variance. Measurement variance is the variance inherent in the data acquisition process. Johnson noise, shot noise, electronic noise and quantization errors are all types of measurement variance or measurement noise. Physiological variances are typically defined by the chemical or structural complexity of the sample. In the preferred application of noninvasive glucose measurement, significant physiological variance is present due to the complex nature of skin. Instrument variance is any variance due to changes in the performance of the optical measurement system. Changes in the performance of the illumination system would be a type of instrument variation. Sampling variance is due to errors associated with optical interfacing to the sample of interest. An objective in designing optical measurement instrumentation is to maximize the net analyte signal. The net analyte signal is that portion of the pure component that is orthogonal to the other sources of spectroscopic variation. The pure component spectrum is the absorbance spectrum of the analyte of interest in the absence of other absorbing species. See, for example, “Net Analyte Signal Calculation in Multivariate Calibration,” by Avraham Lorber, Analytical Chemistry Analytical Chemistry, Vol. 69, No. 8, Apr. 15, 1997.
Practitioners skilled in the art have designed measurement systems that minimize measurement variances associated with the recoding of optical information. In particular, Johnson noise and electronic noise may be minimized through effective instrument design. Additionally, the operation of modem A/D converters using 16-bit or higher digitization can substantially reduce the effect of digitization error. Thus, through instrument design, shot noise can be isolated as the predominant noise source in most modem spectrophotometer systems working in the near-infrared region of the electromagnetic spectra.
Physiological variances are due to compositional or structural differences that vary in the biological sample but are unrelated to the analyte or attribute of interest. Significant complexity is present when the sample of interest is tissue. Biological tissue is commonly characterized as a turbid medium. Turbid media generally fail to permit any single ray of light from following an undisturbed pathway through the medium. In effect, turbid media are non-deterministic. That is, a light ray that enters a turbid medium may undergo several scattering events before finally exiting the medium. When many light rays are directed into a turbid medium, each of the exiting rays collected at any given point will have traveled a different distance through the medium. As a result, a spectrum from a turbid medium source is determined not only by type and concentration of the molecular species in the medium, but also by the scattering characteristics of the media that influence the path distribution of the light traveling through the medium.
Instrumentation variances are spectroscopic variations introduced by the optical instrumentation. Instrumentation variances can result in statistically relevant errors. A recognized instrumentation variant is the illumination system. The illumination system is the entire optical system from the source of radiation to the sample or the spectrometer. Common components of an illumination system include the emitter or radiation source, reflective optics, refractive optics, focusing lenses, collimating lenses, filters, relay optics or optical fibers. A radiation source for absorption measurements must generate a beam with sufficient power in the wavelength region of interest to permit ready detection and measurement. In addition, the radiation source must also provide a reproducible output.
The most common source of near-infrared radiation is the tungsten-filament lamp. The energy distribution of this source approximates that of a blackbody, and is thus temperature dependent. In most absorption spectrophotometers, the operating filament temperature is about 2900 K; therefore, the bulk of the energy is emitted in the infrared region. A tungsten-filament lamp is useful for the wavelength regions between 320 and 2500 nm, the lower limit imposed by absorption by the glass envelope that houses the filament.
A change in the lamp, adjustment to the lamp or changes within a lamp may affect the resulting spectral data produced by the illumination system. The following are four examples of common variants associated with the illumination system. The replacement of the lamp can result in significant spectroscopic variance when using existing illumination systems. The spectroscopic variance can be due to manufacturing inconsistencies between lamp filaments. Known manufacturing inconsistencies include differences in filament shape, differences in filament location, and differences in filament material. A second source of spectroscopic variance can be caused by rotation or tilting of the same lamp in the lamp housing. A third known source of variance is due to differences in the glass envelope surrounding the filament. Specifically the glass envelope “nipple” can create shadowing and cause inhomogenous illumination of the sample or sampling apparatus. Finally, the lamp may change over time due to vibration or sagging of the filament. Such changes can cause intensity and temperature variations along the filament length. With current illumination systems, the above changes can cause spectroscopic variances. In maximizing overall system performance, it is desirable to minimize spectroscopic variances unrelated to the analyte of interest. Illumination system variances include all variances due to different lamps, due to lamp aging, due to placement of the lamp in the instrument or any other variance that results due to a change in lamp performance or how the radiation source interacts with the remainder of the optical system. With current illumination systems, radiation emitter variances can cause spectroscopic variances. Illumination system variances unrelated to either the sample of interest or the analyte being measured can result in prediction errors and necessitate the need for re-calibration. Re-calibration is generally undesirable due to increased expense and down time on the instrument. Thus, in maximizing overall system performance, it is desirable to minimize illumination system variances.
To achieve increased accuracy in the measurement of analyte concentration, a practitioner of the art must, among other things, strive either to eliminate interferents or to construct a chemometric model that is sensitive to the differences between interferents and the desired analyte. Fortunately, spectral changes due to interferents are seldom identical to spectral changes due to changes in analyte concentration. Thus, the ability of a chemometric model to distinguish between an interferent and a particular analyte is typically improved by increasing the size of the calibration set.
In the presence of significant spectroscopic variance and when the degree of overlap (spectral similarity) between the analyte and the interferent is high, the number of model “factors” required to adequately distinguish between the interferent and the analyte will be large (the model complexity will be high). Unfortunately, in the presence of measurement noise, there are practical limitations associated with the number of model factors that can be used effectively. The ability of the practitioner to improve the sensitivity of the model to differences between the interferent and the analyte by increasing the size of the calibration model will be limited by the presence of noise in the measurement which limits the ability to distinguish between the spectra of the interferent and the analyte. In systems where there is a high degree of overlap between interferents and the particular analyte of interest, the practitioner must strive to reduce the amount of spectroscopic interference, specifically instrument variance to the greatest degree possible.
In spectrophotometer instruments where shot noise is the predominant source of measurement noise in the instrument, the signal-to-noise ratio (SNR) for the instrument is directly proportional to the square root of the flux (Φ) on the photodetector. Thus, for these instruments, the SNR can be improved by maximizing the amount of light incident on the detector. For measurements on biological tissue, however, the practitioner cannot increase the flux on the detector without limit. Increasing the flux on the detector generally necessitates increasing the incidance on the tissue. The increased incidence on the tissue may result in thermal damage to the tissue. Therefore, there are practical limits on how much light can be incident on the tissue.
Fourier-transform infrared (FTIR) spectrophotometers are a class of spectrometer that can be operated where shot noise is the predominant measurement noise. FTIR spectrophotometers offer the advantages of unusually high sensitivity, resolution, and speed of data acquisition. Generally, data from an entire spectrum can be obtained in one second or less. The heart of a Fourier transform spectrophotometer is an interferometer, which is a device for analyzing the frequencies present in a composite signal and the relative strength of the signal at such frequencies.
Vast improvement in spectroscopic optical analysis, particularly quantitative determination of analytes in biological tissue, can be achieved if the above-identified problems are minimized or eliminated. In particular, if illumination system variances could be eliminated or substantially reduced as an interferent the complexity (number of model factors) of the model may be reduced and the net analyte signal increased. This would result in reduced instrument variance and increase the predictive accuracy of a model, particularly in situations where the interferent has substantial overlap with the analyte of interest in the tissue. It is further important that a system which eliminates or reduces instrument interferents also minimizes measurement noise. The present invention is directed to apparatus and methods which eliminate or reduce changes in the light source or illumination system as an interferent while maintaining high signal-to-noise ratio.